
Exploration & Production Technologies
Reservoir Characterization - Modeling
In recent years there have been significant advances in the use of geophysical and geological information for reservoir delineation and evaluation. The chief objective of the reservoir engineer is to interpret this information and determine not only the volume of oil and gas within the reservoir but also the most economic recovery methods needed to produce the reserves. The characteristics of the reservoir, such as pay thickness; porosity, or pore space; and fluid types, are needed to calculate the reserves of oil and gas. As more wells are drilled and more information is gathered, the reservoir engineer can refine the estimates of the recoverable resource. This information allows for informed decisions as to whether the reservoir is amenable to secondary and tertiary recovery processes.
Computer simulators are able to refine these data and present 2-D and 3-D images or models of the reservoir for interpretation of the subsurface structures and hydrocarbon-trapping mechanisms of the reservoir. Changes in the character and rock type of the reservoir from both a vertical and horizontal perspective can be input into the simulator to better understand the factors that ultimately determine how much oil and gas will be produced.
Increasing a field’s production, using advanced technologies of improved oil recovery processes, involves numerical modeling of such processes to minimize the risk involved in development or enhanced recovery decisions. Operators want some assurance that investing in technologies such as chemical flooding or in-situ combustion will result in a successful venture. The oil industry continues to require much more detailed analyses from fewer resources to reduce the risk of future investment decisions. This results in an increasing demand for reservoir simulators that use geological and physical models of much greater detail than in the past.
Reservoir simulation provides oil and gas producers with the ability to predict results and optimize processes prior to making a full field investment. Accurate prediction of the timing and amount of oil recovery can mean the difference between profitable and unprofitable operations. Increased computational capacity now allows for simulators with millions of reservoir grid cells in characterizing a reservoir’s geology. Greater accuracy also is required for mapping the compositional flows within the reservoir. These requirements impose tremendous mathematical and computational challenges, with key work being supported by NETL.
NETL support has been provided in four key technology areas:
- Parallel computing is a technique that allows users to execute the simulation in parallel and distribute the tasks across multiple machines on a network. The result is much faster simulation times.
- Streamline simulation is a technique for flow modeling that complements standard finite-difference methods. Streamline simulation is faster than finite-difference methods and provides novel information such as flow visualization, well drainage regions, and well allocation factors—all useful in history-matching and optimizing field performance.
- Adaptive mesh refinement is a computational technique for improving the efficiency of numerical simulations by concentrating effort on computationally demanding regions of a system. The basic idea is to refine, both in space and in time, regions of the computational area that require high resolution to resolve developing features, while leaving less interesting parts at lower resolutions. The algorithm adaptively creates and destroys finer grids, depending on where extra resolution is needed in the reservoir model.
- Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or by other computer techniques. Neural networks take a different approach to problem-solving than that of conventional computers. Computers use an algorithmic approach, whereby the computer follows a set of instructions in order to solve a problem. Neural networks learn by example, rather than being programmed to perform a specific task.
These are just some of the techniques applied to develop predictive models for steamflooding, in-situ combustion, chemical floods, carbon dioxide miscible floods, waterflooding, and well placement. Additional work is being done in the area of fractured-reservoir characterization.
NETL has supported the development of many reservoir simulation products, including BOAST (Black Oil Applied Simulation Tool), UTCHEM, UTCOMP, GEO-SEQ, MASTER (Miscible Applied Simulation Techniques for Energy Recovery), and several other EOR predictive models.
There are commercial reservoir models and simulators available to the petroleum industry, but because of their expense and the user skills necessary to operate the simulators their use is generally restricted to the larger companies who can afford them. NETL recognized that many small operators also needed the capability to fully characterize their producing reservoirs, and to this end computer models such as BOAST and GEMINI were developed. These reservoir simulators are free to the industry, and the software has been designed to be user friendly.
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